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Title: Computing irregularity for features in medical images
Author: Aribisala, Benjamin Segun.
ISNI:       0000 0001 3427 2053
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2006
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Regular and symmetric forms in nature are associated with health and perfection. In medical diagnosis the presence of irregular features often suggests an abnormality. Earlier research identified the irregularity of a lesion border as the most significant factor in the diagnosis of malignant melanoma. Despite its importance, the visual assessment of irregularity is difficult. The work described in the thesis was undertaken with the aim of finding objective computable measures of contour irregularity and their application to characterisation of pigmented skin lesions. A descriptive definition of irregularity was formulated, drawing on the related concepts from the fields of geometry, information theory, statistics, probability, frequency analysis and pattern theory. It defines irregularity in terms of the five attributes: departure from a typical sequence (deviation), lack of obvious description, lack of compressibility, lack of symmetry and lack of a rule for generating a sequence. Skin lesions were found to be characterised by irregularity due to deviation, lack of obvious description and lack of a rule. The ellipse was identified as the underlying regular model and the variability of the lesion boundary was found to be best represented by Weibull distribution. Three methods for computing irregularity according to the above attributes were implemented, evaluated and tested. They were based on the Hidden Markov Models, the Conditional Entropy and the Pattern Theory. The latter method is entirely novel. Within each method the irregularity measures invariant to the form of boundary representation and to the parameterisation of each method were identified through experiments on simulated irregular patterns. The predictive power of the methods as classifiers of lesion abnormality was tested on a set of 98 real lesion outlines with histologically confirmed diagnosis. All the methods showed sensitivity and specificity of over 0.7. The Weibull based Hidden Markov Models method performed best, with both sensitivity and specificity of 0.82. Ranking experiments carried out to investigate whether the computed measures corresponds to the human perception of the border irregularity showed high consistency among the observers (rank correlation coefficient W=0.886). The correlation between the observations and the computed measured varied from W=0.49 for the Hidden Markov Models based measure to W=0.95 for the measure based on the Pattern Theory.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available